mlsanityData to 7 Oct 2026

Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey

arXiv 2005.04275 ↗First posted 8 May 2020cs.LG stat.MLcited by 26

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Abstract (arXiv)

With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices. In this paper, we provide a comprehensive survey on Pruning, a major compression strategy that removes non-critical or redundant neurons from a CNN model. The survey covers the overarching motivation for pruning, different strategies and criteria, their advantages and drawbacks, along with a compilation of major pruning techniques. We conclude the survey with a discussion on alternatives to pruning and current challenges for the model compression community.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2020.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 6; 2024: 9; 2025: 1.

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  1. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 200
  2. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  3. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  4. 2021Adaptive Inference through Early-Exit Networks: Design, Challenges and Directionscited by 74
  5. 2024AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computingcited by 15
  6. 2021Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offsetcited by 9
  7. 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowscited by 8
  8. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  9. 2024TinySV: Speaker Verification in TinyML with On-device Learningcited by 5
  10. 2021Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimizationcited by 4
  11. 2024Neuralink: Fast LLM Inference on Smartphones with Neuron Co-Activation Linkingcited by 4
  12. 2024Low-Energy On-Device Personalization for MCUscited by 3
14 more
  1. 2024Early-Exit meets Model-Distributed Inference at Edge Networkscited by 3
  2. 2022CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Executioncited by 2
  3. 2022Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learningcited by 2
  4. 2023Gated Compression Layers for Efficient Always-On Modelscited by 2
  5. 2024From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networkscited by 2
  6. 2024Priority-Aware Model-Distributed Inference at Edge Networkscited by 2
  7. 2023Efficient Verification-Based Face Identificationcited by 1
  8. 2024Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacycited by 1
  9. 2025Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devicescited by 1
  10. 2021Neural Networks for Infectious Diseases Detection: Prospects and Challengescited by 0
  11. 2022FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devicescited by 0
  12. 2023Transferability of Winning Lottery Tickets in Neural Network Differential Equation Solverscited by 0
  13. 2023Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimatescited by 0
  14. 2024Acceleration Algorithms in GNNs: A Surveycited by 0